This chapter builds on Chapter 4 and introduces two main applications within applied linguistics stemming from a systemic-functional semiotic theory of multimodality: embodied teaching and language textbook analysis. The chapter first gives a brief overview of the broader field of research that each of these applications is located in. Then each focal area is elaborated and illustrated via a case study conducted by the authors. Each case study provides a rationale for why multimodal analysis is appropriate given the research focus and questions, demonstrates how multimodal analysis was implemented and conducted, and reflects on the challenges of its implementation in applied linguistic research contexts.
Objective
To investigate the clinical features and influencing factors of somatic symptoms in patients with acute stroke.
Methods
Patients with acute stroke were enrolled in the study. Using the scores of symptom checklist 90(SCL-90)-somatization factor part, the patients were divided into either a somatic symptom group(≥24)or a control group(<24). Their age, gender, economic level, education level, underlying diseases, Hamilton Anxiety Scale(HAMA), Hamilton Depression Scale(HAMD), NEO Five-Factor Inventory scores, Social Support Rating Scale scores-simplified Chinese version, Mini-Mental State Examination(MMSE)scores, National Institute of Health Stroke Scale(NIHSS)scores were documented and analyzed.
Results
A total of 70 patients with acute stroke were enrolled, and 33(47.1%)of them had somatic symptoms. There were no significant differences in demographic characteristics, education level, family income, occupation, marital status, living alone, residence, medical expenses payment methods and social support scores between the somatic symptom group and the control group. There were also no significant differences in the types of stroke, lesion side, baseline NIHSS score, MMSE score, and NEO Five-Factor Inventory score between the 2 groups. There was significant difference in lesion side between the somatic symptom group and the control group(χ2=0.161, P=0.006). The comparison of neuropsychological test results showed that the proportion of patients with an anxiety state of the somatic symptom group was significantly higher than that of the control group(24.2% vs. 5.4%; χ2=5.055, P=0.025), but there was no significant difference in the proportion of patients with depression status; after excluding the cases who met the anxiety and depression criteria, HAMA(8.08±3.12 vs. 5.58±3.06; t=-3.059, P=0.003)and HAMD(10.80±4.81 vs. 7.73±3.88; t=-2.694, P=0.009)scores of the somatic symptom group(n=25)were significantly higher than those of the control group(n=33). The number of somatic symptoms of the somatic symptom group was significantly more than that of the control group(Z=-5.817, P<0.001), and was more likely to have pain symptoms(97.0% vs. 73.0%; χ2=7.584, P=0.006). The correlation analysis showed that there was a significant positive correlation in the 12-item scores of the SCL-90 somatic factors and HAMA(r=0.494, P<0.001)and HAMD(r=0.369, P=0.002)scores in patients. Multivariate logistic regression analysis showed that HAMA score was an independent risk factor for somatic symptoms after stroke.(odds ratio 1.286, 95% confidence interval 1.060-1.560; P=0.011).
Conclusions
The incidence of somatic symptoms is high after acute stroke, especially in patients with cortical stroke. The somatic patients after stroke are prone to have pain-related symptoms. The patients with anxiety and depression status after stroke are prone to have somatic symptoms after stroke; HAMA score is an independent risk factor for having somatic symptoms after stroke.
Key words:
Stroke; Somatoform Disorders; Depression; Anxiety Disorders; Pain; Risk Factors
Nowadays, item-based Collaborative Filtering (CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical item-based CF approaches is that all users have the same weight in computing the item relationships. In order to improve the recommendation quality, we incorporate users’ weights based on a relationship model of users into item similarities and differentials computing. In this paper, a model of user relationship, a method for computing users’ weights, and weight-based item-item similarities/differentials computing approaches are proposed for item-based CF recommendations. Finally, we experimentally evaluate our approach for recommendation and compare it to typical item-based CF approaches based on Adjusted Cosine and Slope One. The experiments show that our approaches can improve the recommendation results of them.
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations. X-IQE utilizes a hierarchical Chain of Thought (CoT) to enable MiniGPT-4 to produce self-consistent, unbiased texts that are highly correlated with human evaluation. It offers several advantages, including the ability to distinguish between real and generated images, evaluate text-image alignment, and assess image aesthetics without requiring model training or fine-tuning. X-IQE is more cost-effective and efficient compared to human evaluation, while significantly enhancing the transparency and explainability of deep image quality evaluation models. We validate the effectiveness of our method as a benchmark using images generated by prevalent diffusion models. X-IQE demonstrates similar performance to state-of-the-art (SOTA) evaluation methods on COCO Caption, while overcoming the limitations of previous evaluation models on DrawBench, particularly in handling ambiguous generation prompts and text recognition in generated images. Project website: https://github.com/Schuture/Benchmarking-Awesome-Diffusion-Models
This paper proposes three training strategies based on impedance control, including passive training, damping-active training and spring-active training, for a 3-DOF lower limb rehabilitation robot designed for patients with paraplegia or hemiplegia. Controllers with similar structure are developed for these training strategies, consisting of dual closed loops, the outer impedance control loop and the inner position/velocity control loop, known as position-based impedance control method. Simulation results verify that position-based impedance control approach is feasible to accomplish the training strategies.
Summary d ‐Xylose is the most abundant fermentable pentose in nature and can serve as a carbon source for many bacterial species. Since d ‐xylose constitutes the major component of hemicellulose, its metabolism is important for lignocellulosic biomass utilization. Here, we report a six‐protein module for d ‐xylose signaling, uptake and regulation in solvent‐producing C lostridium beijerinckii . This module consists of a novel ‘three‐component system’ (a putative periplasmic ABC transporter substrate‐binding protein XylFII and a two‐component system LytS / YesN ) and an ABC ‐type d ‐xylose transporter XylFGH . Interestingly, we demonstrate that, although XylFII harbors a transmembrane domain, it is not involved in d ‐xylose transport. Instead, XylFII acts as a signal sensor to assist the response of LytS / YesN to extracellular d ‐xylose, thus enabling LytS / YesN to directly activate the transcription of the adjacent xylFGH genes and thereby promote the uptake of d ‐xylose. To our knowledge, XylFII is a novel single transmembrane sensor that assists two‐component system to respond to extracellular sugar molecules. Also of significance, this ‘three‐component system’ is widely distributed in F irmicutes, indicating that it may play a broad role in this bacterial phylum. The results reported here provide new insights into the regulatory mechanism of d ‐xylose sensing and transport in bacteria.
In recent years, with the development of quantum machine learning, quantum neural networks (QNNs) have gained increasing attention in the field of natural language processing (NLP) and have achieved a series of promising results. However, most existing QNN models focus on the architectures of quantum recurrent neural network (QRNN) and self-attention mechanism (QSAM). In this work, we propose a novel QNN model based on quantum convolution. We develop the quantum depthwise convolution that significantly reduces the number of parameters and lowers computational complexity. We also introduce the multi-scale feature fusion mechanism to enhance model performance by integrating word-level and sentence-level features. Additionally, we propose the quantum word embedding and quantum sentence embedding, which provide embedding vectors more efficiently. Through experiments on two benchmark text classification datasets, we demonstrate our model outperforms a wide range of state-of-the-art QNN models. Notably, our model achieves a new state-of-the-art test accuracy of 96.77% on the RP dataset. We also show the advantages of our quantum model over its classical counterparts in its ability to improve test accuracy using fewer parameters. Finally, an ablation test confirms the effectiveness of the multi-scale feature fusion mechanism and quantum depthwise convolution in enhancing model performance.
Abstract Monkeypox, caused by the monkeypox virus (MPXV), was historically confined to West and Central Africa but has now spread globally. Recombination and selection play crucial roles in the evolutionary adaptation of MPXV; however, the evolution of MPXV and its relationship with the recent, ground‐breaking monkeypox epidemic remains poorly understood. To gain insights into the evolutionary dynamics of MPXV, comprehensive in silico recombination and selection analyses were conducted based on MPXV whole genome sequence data. Three types of recombination were identified: five ancestor‐sharing interspecies recombination events, six specific interspecies recombination events and four intraspecies recombination events. The results highlight the prevalent occurrence of recombination in MPXV, with 73.3% occurring in variable regions of the genome. Selection analysis was performed from three dimensions: proteins around recombination regions, proteins from recombinant ancestors and MPXV branches, and whole‐genome gene analysis. Results revealed 2 and 7 proteins under positive selection in the first two dimensions, respectively. These proteins are mainly involved in infection immunity, apoptosis regulation and viral virulence. Whole‐genome analysis detected 25 genes under positive selection, mainly associated with immune response and viral regulation. Understanding their evolutionary patterns will help predict and prevent cross‐species transmission, zoonotic outbreaks and potential human epidemics.
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